{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# CIFAR100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch\n",
    "import torchvision\n",
    "import torchvision.transforms as transforms\n",
    "import torch.optim as optim\n",
    "import torch.nn as nn\n",
    "from torchvision import datasets"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## load data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "batch_size = 128\n",
    "input_size = 32\n",
    "\n",
    "data_transforms = {\n",
    "    'train': transforms.Compose([\n",
    "        transforms.RandomHorizontalFlip(0.5),\n",
    "        transforms.RandomCrop(32, 4),\n",
    "        transforms.ToTensor(),\n",
    "        transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])\n",
    "    ]),\n",
    "    'test': transforms.Compose([\n",
    "        transforms.Resize(input_size),\n",
    "        transforms.CenterCrop(input_size),\n",
    "        transforms.ToTensor(),\n",
    "        transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])\n",
    "    ]),\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "391\n"
     ]
    }
   ],
   "source": [
    "trainset = torchvision.datasets.CIFAR100(root='./data', train=True,\n",
    "            download=False, transform=data_transforms['train'])\n",
    "trainloader = torch.utils.data.DataLoader(trainset, batch_size=batch_size,\n",
    "            shuffle=True, num_workers=2)\n",
    "\n",
    "testset = torchvision.datasets.CIFAR100(root='./data', train=False,\n",
    "            download=False, transform=data_transforms['test'])\n",
    "testloader = torch.utils.data.DataLoader(testset, batch_size=batch_size,\n",
    "            shuffle=False, num_workers=2)\n",
    "print(len(trainloader))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "classes = ('apple', 'aquarium_fish', 'baby', 'bear', 'beaver', 'bed', 'bee', 'beetle',\n",
    "           'bicycle', 'bottle', 'bowl', 'boy', 'bridge', 'bus', 'butterfly', 'camel',\n",
    "           'can', 'castle', 'caterpillar', 'cattle', 'chair', 'chimpanzee', 'clock',\n",
    "           'cloud', 'cockroach', 'couch', 'crab', 'crocodile', 'cup', 'dinosaur',\n",
    "           'dolphin', 'elephant', 'flatfish', 'forest', 'fox', 'girl', 'hamster',\n",
    "           'house', 'kangaroo', 'keyboard', 'lamp', 'lawn_mower', 'leopard', 'lion',\n",
    "           'lizard', 'lobster', 'man', 'maple_tree', 'motorcycle', 'mountain', 'mouse',\n",
    "           'mushroom', 'oak_tree', 'orange', 'orchid', 'otter', 'palm_tree', 'pear',\n",
    "           'pickup_truck', 'pine_tree', 'plain', 'plate', 'poppy', 'porcupine',\n",
    "           'possum', 'rabbit', 'raccoon', 'ray', 'road', 'rocket', 'rose',\n",
    "           'sea', 'seal', 'shark', 'shrew', 'skunk', 'skyscraper', 'snail', 'snake',\n",
    "           'spider', 'squirrel', 'streetcar', 'sunflower', 'sweet_pepper', 'table',\n",
    "           'tank', 'telephone', 'television', 'tiger', 'tractor', 'train', 'trout',\n",
    "           'tulip', 'turtle', 'wardrobe', 'whale', 'willow_tree', 'wolf', 'woman',\n",
    "           'worm')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "forest leopard sweet_pepper shrew telephone chair telephone crab \n",
      "beaver lion  wolf  mushroom lizard girl  wolf  bed  \n",
      "sunflower wardrobe house tank  tulip girl  mouse kangaroo\n",
      "boy   mountain television crab  whale bed   crab  lizard\n",
      "crab  elephant pear  trout pickup_truck tulip flatfish beetle\n",
      "crab  table mushroom bus   skyscraper table snail bottle\n",
      "tiger forest cup   lobster poppy trout worm  hamster\n",
      "snail bicycle butterfly bee   sunflower palm_tree fox   leopard\n",
      "wolf  butterfly orchid leopard bus   crocodile maple_tree ray  \n",
      "snail crocodile crocodile lawn_mower bee   camel cattle chimpanzee\n",
      "poppy sea   orchid woman train pickup_truck table willow_tree\n",
      "house orange maple_tree poppy train caterpillar camel snake\n",
      "boy   table ray   hamster tiger bicycle flatfish skyscraper\n",
      "willow_tree lobster bee   trout skunk train keyboard porcupine\n",
      "plate bus   porcupine whale cup   porcupine tractor skunk\n",
      "girl  telephone wolf  girl  caterpillar trout orchid rocket\n"
     ]
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "# functions to show an image\n",
    "def imshow(img):\n",
    "    img = img / 2 + 0.5     # unnormalize\n",
    "    npimg = img.numpy()\n",
    "    plt.imshow(np.transpose(npimg, (1, 2, 0)))\n",
    "    plt.show()\n",
    "\n",
    "\n",
    "# get some random training images\n",
    "dataiter = iter(trainloader)\n",
    "images, labels = dataiter.next()\n",
    "\n",
    "# show images\n",
    "imshow(torchvision.utils.make_grid(images))\n",
    "# print labels in grid\n",
    "labels_grid = np.array(labels).reshape(batch_size//8, 8)\n",
    "for row in labels_grid:\n",
    "    print(' '.join(f'{classes[label]:5s}' for label in row))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## define model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cuda\n",
      "----------------------------------------------------------------\n",
      "        Layer (type)               Output Shape         Param #\n",
      "================================================================\n",
      "            Conv2d-1           [-1, 64, 16, 16]           9,472\n",
      "       BatchNorm2d-2           [-1, 64, 16, 16]             128\n",
      "              ReLU-3           [-1, 64, 16, 16]               0\n",
      "         MaxPool2d-4             [-1, 64, 8, 8]               0\n",
      "            Conv2d-5             [-1, 64, 8, 8]          36,928\n",
      "       BatchNorm2d-6             [-1, 64, 8, 8]             128\n",
      "            Conv2d-7             [-1, 64, 8, 8]          36,928\n",
      "       BatchNorm2d-8             [-1, 64, 8, 8]             128\n",
      "          Residual-9             [-1, 64, 8, 8]               0\n",
      "           Conv2d-10             [-1, 64, 8, 8]          36,928\n",
      "      BatchNorm2d-11             [-1, 64, 8, 8]             128\n",
      "           Conv2d-12             [-1, 64, 8, 8]          36,928\n",
      "      BatchNorm2d-13             [-1, 64, 8, 8]             128\n",
      "         Residual-14             [-1, 64, 8, 8]               0\n",
      "           Conv2d-15            [-1, 128, 4, 4]          73,856\n",
      "      BatchNorm2d-16            [-1, 128, 4, 4]             256\n",
      "           Conv2d-17            [-1, 128, 4, 4]         147,584\n",
      "      BatchNorm2d-18            [-1, 128, 4, 4]             256\n",
      "           Conv2d-19            [-1, 128, 4, 4]           8,320\n",
      "         Residual-20            [-1, 128, 4, 4]               0\n",
      "           Conv2d-21            [-1, 128, 4, 4]         147,584\n",
      "      BatchNorm2d-22            [-1, 128, 4, 4]             256\n",
      "           Conv2d-23            [-1, 128, 4, 4]         147,584\n",
      "      BatchNorm2d-24            [-1, 128, 4, 4]             256\n",
      "         Residual-25            [-1, 128, 4, 4]               0\n",
      "           Conv2d-26            [-1, 256, 2, 2]         295,168\n",
      "      BatchNorm2d-27            [-1, 256, 2, 2]             512\n",
      "           Conv2d-28            [-1, 256, 2, 2]         590,080\n",
      "      BatchNorm2d-29            [-1, 256, 2, 2]             512\n",
      "           Conv2d-30            [-1, 256, 2, 2]          33,024\n",
      "         Residual-31            [-1, 256, 2, 2]               0\n",
      "           Conv2d-32            [-1, 256, 2, 2]         590,080\n",
      "      BatchNorm2d-33            [-1, 256, 2, 2]             512\n",
      "           Conv2d-34            [-1, 256, 2, 2]         590,080\n",
      "      BatchNorm2d-35            [-1, 256, 2, 2]             512\n",
      "         Residual-36            [-1, 256, 2, 2]               0\n",
      "           Conv2d-37            [-1, 512, 1, 1]       1,180,160\n",
      "      BatchNorm2d-38            [-1, 512, 1, 1]           1,024\n",
      "           Conv2d-39            [-1, 512, 1, 1]       2,359,808\n",
      "      BatchNorm2d-40            [-1, 512, 1, 1]           1,024\n",
      "           Conv2d-41            [-1, 512, 1, 1]         131,584\n",
      "         Residual-42            [-1, 512, 1, 1]               0\n",
      "           Conv2d-43            [-1, 512, 1, 1]       2,359,808\n",
      "      BatchNorm2d-44            [-1, 512, 1, 1]           1,024\n",
      "           Conv2d-45            [-1, 512, 1, 1]       2,359,808\n",
      "      BatchNorm2d-46            [-1, 512, 1, 1]           1,024\n",
      "         Residual-47            [-1, 512, 1, 1]               0\n",
      "AdaptiveAvgPool2d-48            [-1, 512, 1, 1]               0\n",
      "           Linear-49                  [-1, 100]          51,300\n",
      "================================================================\n",
      "Total params: 11,230,820\n",
      "Trainable params: 11,230,820\n",
      "Non-trainable params: 0\n",
      "----------------------------------------------------------------\n",
      "Input size (MB): 0.01\n",
      "Forward/backward pass size (MB): 1.02\n",
      "Params size (MB): 42.84\n",
      "Estimated Total Size (MB): 43.88\n",
      "----------------------------------------------------------------\n"
     ]
    }
   ],
   "source": [
    "from resnet import ResNet18\n",
    "from torchsummary import summary\n",
    "\n",
    "model = ResNet18(3, 100)\n",
    "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
    "print(device)\n",
    "model.to(device)\n",
    "\n",
    "summary(model, (3, 32, 32))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## define loss function and optimizer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "learning_rate = 0.1\n",
    "weight_decay = 1e-4\n",
    "\n",
    "params = [\n",
    "    {'params': [p for n, p in model.named_parameters() if 'bias' not in n], 'weight_decay': weight_decay},\n",
    "    {'params': [p for n, p in model.named_parameters() if 'bias' in n], 'weight_decay': 0}\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "criterion = nn.CrossEntropyLoss()\n",
    "optimizer = optim.SGD(params, lr=learning_rate, momentum=0.9)\n",
    "\n",
    "warmup_epochs = 10\n",
    "lr_lambda = lambda epoch: epoch / warmup_epochs if epoch < warmup_epochs else 1\n",
    "\n",
    "warmup_scheduler = torch.optim.lr_scheduler.LambdaLR(optimizer, lr_lambda)\n",
    "lr_scheduler = optim.lr_scheduler.MultiStepLR(optimizer, milestones=[30, 60, 80], gamma=0.1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## training function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "def train_one_epoch(epoch, model, trainloader, criterion, optimizer, losses, times):\n",
    "    model.train()\n",
    "    running_loss = 0.0\n",
    "    for i, data in enumerate(trainloader, 0):\n",
    "        # get the inputs; data is a list of [inputs, labels]\n",
    "        inputs, labels = data[0].to(device), data[1].to(device)\n",
    "\n",
    "        # zero the parameter gradients\n",
    "        optimizer.zero_grad()\n",
    "\n",
    "        # forward + backward + optimize\n",
    "        outputs = model(inputs)\n",
    "        loss = criterion(outputs, labels)\n",
    "        loss.backward()\n",
    "        optimizer.step()\n",
    "\n",
    "        # print statistics\n",
    "        running_loss += loss.item()\n",
    "        if i % times == times-1:\n",
    "            print(f'[{epoch + 1}, {i + 1:5d}] loss: {running_loss / times:.3f}')\n",
    "            losses.append(running_loss / times)\n",
    "            running_loss = 0.0\n",
    "\n",
    "def eval_model(model, testloader, accuracys1, accuracys5, topk=(1,5)):\n",
    "    model.eval()\n",
    "    correct_1 = 0\n",
    "    correct_5 = 0\n",
    "    total = 0\n",
    "    with torch.no_grad():\n",
    "        for data in testloader:\n",
    "            images, labels = data[0].to(device), data[1].to(device)\n",
    "            outputs = model(images)\n",
    "            _, pred = outputs.topk(max(topk), 1, True, True)\n",
    "            pred = pred.t()\n",
    "            correct = pred.eq(labels.view(1, -1).expand_as(pred))\n",
    "\n",
    "            for k in topk:\n",
    "                correct_k = correct[:k].reshape(-1).float().sum(0, keepdim=True).item()\n",
    "                if k == 1:\n",
    "                    correct_1 += correct_k\n",
    "                elif k == 5:\n",
    "                    correct_5 += correct_k\n",
    "            total += labels.size(0)\n",
    "\n",
    "    print(f'Top-1 accuracy of the network on the 10000 test images: {100 * correct_1 // total} %')\n",
    "    print(f'Top-5 accuracy of the network on the 10000 test images: {100 * correct_5 // total} %')\n",
    "    accuracys1.append(100 * correct_1 // total)\n",
    "    accuracys5.append(100 * correct_5 // total)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_loss(losses,accuracys1,accuracys5):\n",
    "    plt.figure(figsize=(15,5))\n",
    "\n",
    "    plt.subplot(1, 3, 1)\n",
    "    plt.plot(losses)\n",
    "    plt.xlabel(\"every 128*125 item\")\n",
    "    plt.ylabel(\"loss\")\n",
    "\n",
    "    plt.subplot(1, 3, 2)\n",
    "    plt.plot(accuracys1)\n",
    "    plt.xlabel(\"every epoch\")\n",
    "    plt.ylabel(\"Top-1 accuracy\")\n",
    "\n",
    "    plt.subplot(1, 3, 3)\n",
    "    plt.plot(accuracys5)\n",
    "    plt.xlabel(\"every epoch\")\n",
    "    plt.ylabel(\"Top-5 accuracy\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## train the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "epochs = 100\n",
    "losses = []\n",
    "accuracys1 = []\n",
    "accuracys5 = []"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%time\n",
    "for epoch in range(epochs):\n",
    "    train_one_epoch(epoch, model, trainloader, criterion, optimizer, losses, 16000//batch_size)\n",
    "    eval_model(model, testloader, accuracys1, accuracys5)\n",
    "    # 更新学习率\n",
    "    if epoch < warmup_epochs:\n",
    "        warmup_scheduler.step()\n",
    "    else:\n",
    "        lr_scheduler.step()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "PATH = './resnet18.pth'\n",
    "net = ResNet18(3,100)\n",
    "net.load_state_dict(torch.load(PATH))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## evaluate the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plot_loss(losses,accuracys1,accuracys5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# prepare to count predictions for each class\n",
    "correct_pred = {classname: 0 for classname in classes}\n",
    "total_pred = {classname: 0 for classname in classes}\n",
    "\n",
    "# again no gradients needed\n",
    "with torch.no_grad():\n",
    "    for data in testloader:\n",
    "        images, labels = data\n",
    "        outputs = model(images)\n",
    "        _, predictions = torch.max(outputs, 1)\n",
    "        # collect the correct predictions for each class\n",
    "        for label, prediction in zip(labels, predictions):\n",
    "            if label == prediction:\n",
    "                correct_pred[classes[label]] += 1\n",
    "            total_pred[classes[label]] += 1\n",
    "\n",
    "\n",
    "# print accuracy for each class\n",
    "for classname, correct_count in correct_pred.items():\n",
    "    accuracy = 100 * float(correct_count) / total_pred[classname]\n",
    "    print(f'Accuracy for class: {classname:5s} is {accuracy:.1f} %')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## ViT"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### pretrained ViT for CIFAR100 from hugging face\n",
    "\n",
    "Top-1 Accuracy: 89.85%\n",
    "\n",
    "Top-5 Accuracy: 98.66%"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from transformers import AutoImageProcessor, AutoModelForImageClassification\n",
    "\n",
    "# processor = AutoImageProcessor.from_pretrained(\"Ahmed9275/Vit-Cifar100\")\n",
    "# model = AutoModelForImageClassification.from_pretrained(\"Ahmed9275/Vit-Cifar100\")\n",
    "# 从本地加载模型\n",
    "processor = AutoImageProcessor.from_pretrained(\"./Vit-Cifar100\")\n",
    "model = AutoModelForImageClassification.from_pretrained(\"./Vit-Cifar100\")\n",
    "\n",
    "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
    "model.to(device)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# CIFAR-100 数据集的预处理和加载\n",
    "transform = transforms.Compose([\n",
    "    transforms.Resize((224, 224)),  # ViT 模型需要224x224的输入\n",
    "    transforms.ToTensor(),\n",
    "    transforms.Normalize(mean=processor.image_mean, std=processor.image_std)\n",
    "])\n",
    "\n",
    "test_dataset = datasets.CIFAR100(root=\"./data\", train=False, download=False, transform=transform)\n",
    "test_loader = torch.utils.data.DataLoader(test_dataset, batch_size=4, shuffle=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def evaluate(model, dataloader):\n",
    "    model.eval()\n",
    "    correct1 = 0\n",
    "    correct5 = 0\n",
    "    total = 0\n",
    "    with torch.no_grad():\n",
    "        for images, labels in dataloader:\n",
    "            images, labels = images.to(device), labels.to(device)\n",
    "            outputs = model(images).logits\n",
    "            _, top5_predicted = torch.topk(outputs, 5, dim=1)\n",
    "            total += labels.size(0)\n",
    "            correct1 += (top5_predicted[:, 0] == labels).sum().item()\n",
    "            correct5 += sum([1 if labels[i] in top5_predicted[i] else 0 for i in range(labels.size(0))])\n",
    "    top1_accuracy = correct1 / total\n",
    "    top5_accuracy = correct5 / total\n",
    "    return top1_accuracy, top5_accuracy\n",
    "\n",
    "top1_accuracy, top5_accuracy = evaluate(model, test_loader)\n",
    "print(f\"Top-1 Accuracy: {top1_accuracy * 100:.2f}%\")\n",
    "print(f\"Top-5 Accuracy: {top5_accuracy * 100:.2f}%\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### pretrained ViT from torchvision, train the linear layer\n",
    "\n",
    "\n",
    "Top-1 Accuracy: 79.0 %\n",
    "\n",
    "Top-5 Accuracy: 96.0 %"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from torchvision.models import vit_b_16, ViT_B_16_Weights"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 定义ViT模型\n",
    "weights = ViT_B_16_Weights.DEFAULT\n",
    "model = vit_b_16(weights=weights)\n",
    "model.heads[0] = nn.Linear(model.heads[0].in_features, 100)  # 修改分类头为100类"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 冻结前面的层\n",
    "for param in model.parameters():\n",
    "    param.requires_grad = False\n",
    "\n",
    "# 只训练新的分类层\n",
    "for param in model.heads[0].parameters():\n",
    "    param.requires_grad = True\n",
    "\n",
    "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
    "model.to(device)\n",
    "print(device)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "batch_size = 128\n",
    "transform = transforms.Compose([\n",
    "    transforms.Resize((224, 224)),  # ViT期望的输入尺寸\n",
    "    transforms.ToTensor(),\n",
    "    transforms.Normalize(0.5, 0.5)\n",
    "])\n",
    "trainset = torchvision.datasets.CIFAR100(root='./data', train=True,\n",
    "            download=False, transform=transform)\n",
    "trainloader = torch.utils.data.DataLoader(trainset, batch_size=batch_size,\n",
    "            shuffle=True, num_workers=2)\n",
    "\n",
    "testset = torchvision.datasets.CIFAR100(root='./data', train=False,\n",
    "            download=False, transform=transform)\n",
    "testloader = torch.utils.data.DataLoader(testset, batch_size=batch_size,\n",
    "            shuffle=False, num_workers=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "criterion = nn.CrossEntropyLoss()\n",
    "optimizer = optim.Adam(model.parameters(), lr=0.001)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%time\n",
    "epoches = 5\n",
    "losses = []\n",
    "accuracys1 = []\n",
    "accuracys5 = []\n",
    "for epoch in range(epoches):\n",
    "  train_one_epoch(epoch, model, trainloader, criterion, optimizer, losses, 16000//batch_size)\n",
    "  eval_model(model, testloader, accuracys1, accuracys5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plot_loss(losses,accuracys1,accuracys5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### [ViT from scratch](https://colab.research.google.com/drive/11Ofsf3Zgu4wenT5gLfLFN2Xmmuejxs7R?usp=sharing)\n",
    "\n",
    "pretrained ViT on imagenet21k, train the whole model on CIFAR100 for about 2 epochs\n",
    "\n",
    "Top-1 Accuracy: 90.86%\n",
    "\n",
    "Top-5 Accuracy: 98.93%"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from torch.nn import CrossEntropyLoss, Dropout, Softmax, Linear, Conv2d, LayerNorm\n",
    "from torch.nn.modules.utils import _pair\n",
    "import copy\n",
    "import math\n",
    "from os.path import join as pjoin\n",
    "from scipy import ndimage\n",
    "import ml_collections"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 0. Configs\n",
    "为了使用预训练的ViT模型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "IMG_SIZE = 224\n",
    "TRAIN_BATCH_SIZE = 64\n",
    "EVAL_BATCH_SIZE = 16\n",
    "LEARNING_RATE = 3e-2\n",
    "WEIGHT_DECAY = 0\n",
    "NUM_STEPS = 1600\n",
    "WARMUP_STEPS = 300"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "ATTENTION_Q = \"MultiHeadDotProductAttention_1/query\"\n",
    "ATTENTION_K = \"MultiHeadDotProductAttention_1/key\"\n",
    "ATTENTION_V = \"MultiHeadDotProductAttention_1/value\"\n",
    "ATTENTION_OUT = \"MultiHeadDotProductAttention_1/out\"\n",
    "FC_0 = \"MlpBlock_3/Dense_0\"\n",
    "FC_1 = \"MlpBlock_3/Dense_1\"\n",
    "ATTENTION_NORM = \"LayerNorm_0\"\n",
    "MLP_NORM = \"LayerNorm_2\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Returns the ViT-B/16 configuration\n",
    "config = ml_collections.ConfigDict()\n",
    "config.patches = ml_collections.ConfigDict({'size': (16, 16)})\n",
    "config.hidden_size = 768\n",
    "config.transformer = ml_collections.ConfigDict()\n",
    "config.transformer.mlp_dim = 3072\n",
    "config.transformer.num_heads = 12\n",
    "config.transformer.num_layers = 12\n",
    "config.transformer.attention_dropout_rate = 0.0\n",
    "config.transformer.dropout_rate = 0.1\n",
    "config.classifier = 'token'\n",
    "config.representation_size = None"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 1. 数据加载&预处理\n",
    "\n",
    "将32 * 32图像处理为224 * 224"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "transform_train = transforms.Compose([\n",
    "    transforms.RandomResizedCrop((IMG_SIZE, IMG_SIZE), scale=(0.05, 1.0)),\n",
    "    transforms.ToTensor(),\n",
    "    transforms.Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5]),\n",
    "])\n",
    "transform_test = transforms.Compose([\n",
    "    transforms.Resize((IMG_SIZE, IMG_SIZE)),\n",
    "    transforms.ToTensor(),\n",
    "    transforms.Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5]),\n",
    "])\n",
    "\n",
    "trainset = datasets.CIFAR100(root=\"./data\",\n",
    "                                  train=True,\n",
    "                                  download=False,\n",
    "                                  transform=transform_train)\n",
    "testset = datasets.CIFAR100(root=\"./data\",\n",
    "                                train=False,\n",
    "                                download=False,\n",
    "                                transform=transform_test)\n",
    "\n",
    "trainloader = torch.utils.data.DataLoader(trainset, batch_size=TRAIN_BATCH_SIZE,\n",
    "            shuffle=True, num_workers=2)\n",
    "testloader = torch.utils.data.DataLoader(testset, batch_size=EVAL_BATCH_SIZE,\n",
    "            shuffle=False, num_workers=2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 2. 模型定义"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### a. 工具函数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "def np2th(weights, conv=False):\n",
    "    \"\"\"\n",
    "    将 numpy 数组转换为 PyTorch 张量\n",
    "    如果 conv 参数为 True则convert HWIO to OIHW\n",
    "    \"\"\"\n",
    "    if conv:\n",
    "        weights = weights.transpose([3, 2, 0, 1])\n",
    "    return torch.from_numpy(weights)\n",
    "\n",
    "def swish(x):\n",
    "    '''\n",
    "    Swish activation function\n",
    "    '''\n",
    "    return x * torch.sigmoid(x)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### b. Embeddings\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<center>\n",
    "<img src=\"./res/vit_embeddings.png\" width=\"60%\">\n",
    "</center>\n",
    "\n",
    "把图像切成块：  \n",
    "输入224 * 224，每个块16 * 16，于是有196个块  \n",
    "接下来将每个块转为向量表示(NLP中的词向量)，这里为768维  \n",
    "通过卷积操作来是实现，输入224 * 224 * 3，输出14 * 14 * 768，再reshape为196 * 768即是transformer想要的  \n",
    "`Conv2d(in_channels=3, out_channels=768, kernel_size=16, stride=16)`  \n",
    "\n",
    "NLP中使用Bert进行文本分类，会在每个句子前加上一个特殊的token `[CLS]`，输出时取这个token训练得到的向量来分类，**让这个开头的token学习了整个句子的信息**  \n",
    "ViT也模仿这个操作，创造了一个768维的向量来表示这个`[cls]`，输入就变为197 * 768\n",
    "\n",
    "另外，还需要告诉transformer特征向量位于原输入什么位置，即position embedding。ViT中没有使用contact，而是直接加到了原始向量上\n",
    "\n",
    "<center>\n",
    "<img src=\"./res/vit_embeddings_all.png\" width=\"25%\">\n",
    "</center>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "class Embeddings(nn.Module):\n",
    "    def __init__(self, config, img_size, in_channels=3):\n",
    "        super(Embeddings, self).__init__()\n",
    "        img_size = _pair(img_size)\n",
    "        self.hybrid = None\n",
    "        patch_size = _pair(config.patches[\"size\"])\n",
    "        # 计算pathes的数量\n",
    "        n_patches = (img_size[0] // patch_size[0]) * (img_size[1] // patch_size[1])\n",
    "\n",
    "        self.patch_embeddings = Conv2d(in_channels=in_channels,\n",
    "                                       out_channels=config.hidden_size,\n",
    "                                       kernel_size=patch_size,\n",
    "                                       stride=patch_size)\n",
    "        # parameter代表可训练的参数，初始化为0，训练时放入预训练的参数\n",
    "        self.position_embeddings = nn.Parameter(torch.zeros(1, n_patches+1, config.hidden_size))\n",
    "        self.cls_token = nn.Parameter(torch.zeros(1, 1, config.hidden_size))\n",
    "        self.dropout = Dropout(config.transformer[\"dropout_rate\"])\n",
    "\n",
    "    def forward(self, x):\n",
    "        B = x.shape[0]  # batch size\n",
    "        # cls_token: torch.Size([1, 1, 768])\n",
    "        cls_tokens = self.cls_token.expand(B, -1, -1)  # cls_tokens: torch.Size([16, 1, 768])\n",
    "        \n",
    "        x = self.patch_embeddings(x)   # x: torch.Size([16, 768, 14, 14])\n",
    "        x = x.flatten(2)  # x: torch.Size([16, 768, 196])\n",
    "        x = x.transpose(-1, -2)  # x: torch.Size([16, 196, 768])\n",
    "        x = torch.cat((cls_tokens, x), dim=1)  # x: torch.Size([16, 197, 768])\n",
    "\n",
    "        embeddings = x + self.position_embeddings  # add position embeddings\n",
    "        embeddings = self.dropout(embeddings)  # add dropout\n",
    "        return embeddings"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### c. Transformer Encoder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "class Encoder(nn.Module):\n",
    "    def __init__(self, config, vis):\n",
    "        '''\n",
    "        12层encoder block\n",
    "        接一层layer norm\n",
    "        '''\n",
    "        super(Encoder, self).__init__()\n",
    "        self.vis = vis\n",
    "        self.layer = nn.ModuleList()\n",
    "        self.encoder_norm = LayerNorm(config.hidden_size, eps=1e-6)\n",
    "        for _ in range(config.transformer[\"num_layers\"]):\n",
    "            layer = Block(config, vis)\n",
    "            self.layer.append(copy.deepcopy(layer))\n",
    "\n",
    "    def forward(self, hidden_states):\n",
    "        '''\n",
    "        hidden_states即为embedding之后的特征向量\n",
    "        torch.Size([16, 197, 768])\n",
    "        '''\n",
    "        attn_weights = []\n",
    "        for layer_block in self.layer:\n",
    "            hidden_states, weights = layer_block(hidden_states)\n",
    "            if self.vis:\n",
    "                attn_weights.append(weights)\n",
    "        encoded = self.encoder_norm(hidden_states)\n",
    "        return encoded, attn_weights"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### d. Encoder block"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<center>\n",
    "<img src=\"./res/encoder_block.png\" width=\"15%\">\n",
    "</center>\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "class Block(nn.Module):\n",
    "    def __init__(self, config, vis):\n",
    "        super(Block, self).__init__()\n",
    "        self.hidden_size = config.hidden_size\n",
    "        self.attention_norm = LayerNorm(config.hidden_size, eps=1e-6)\n",
    "        self.ffn_norm = LayerNorm(config.hidden_size, eps=1e-6)\n",
    "        self.ffn = Mlp(config)\n",
    "        self.attn = Attention(config, vis)\n",
    "\n",
    "    def forward(self, x):\n",
    "        # LayerNorm -> SelfAttention -> Residual Connection\n",
    "        h = x\n",
    "        x = self.attention_norm(x)\n",
    "        x, weights = self.attn(x)\n",
    "        x = x + h\n",
    "        # LayerNorm -> Feed Forward Networks -> Residual Connection\n",
    "        h = x\n",
    "        x = self.ffn_norm(x)\n",
    "        x = self.ffn(x)\n",
    "        x = x + h\n",
    "        return x, weights\n",
    "\n",
    "    def load_from(self, weights, n_block):\n",
    "        ROOT = f\"Transformer/encoderblock_{n_block}\"\n",
    "        with torch.no_grad():\n",
    "            query_weight = np2th(weights[pjoin(ROOT, ATTENTION_Q, \"kernel\")]).view(self.hidden_size, self.hidden_size).t()\n",
    "            key_weight = np2th(weights[pjoin(ROOT, ATTENTION_K, \"kernel\")]).view(self.hidden_size, self.hidden_size).t()\n",
    "            value_weight = np2th(weights[pjoin(ROOT, ATTENTION_V, \"kernel\")]).view(self.hidden_size, self.hidden_size).t()\n",
    "            out_weight = np2th(weights[pjoin(ROOT, ATTENTION_OUT, \"kernel\")]).view(self.hidden_size, self.hidden_size).t()\n",
    "\n",
    "            query_bias = np2th(weights[pjoin(ROOT, ATTENTION_Q, \"bias\")]).view(-1)\n",
    "            key_bias = np2th(weights[pjoin(ROOT, ATTENTION_K, \"bias\")]).view(-1)\n",
    "            value_bias = np2th(weights[pjoin(ROOT, ATTENTION_V, \"bias\")]).view(-1)\n",
    "            out_bias = np2th(weights[pjoin(ROOT, ATTENTION_OUT, \"bias\")]).view(-1)\n",
    "\n",
    "            self.attn.query.weight.copy_(query_weight)\n",
    "            self.attn.key.weight.copy_(key_weight)\n",
    "            self.attn.value.weight.copy_(value_weight)\n",
    "            self.attn.out.weight.copy_(out_weight)\n",
    "            self.attn.query.bias.copy_(query_bias)\n",
    "            self.attn.key.bias.copy_(key_bias)\n",
    "            self.attn.value.bias.copy_(value_bias)\n",
    "            self.attn.out.bias.copy_(out_bias)\n",
    "\n",
    "            mlp_weight_0 = np2th(weights[pjoin(ROOT, FC_0, \"kernel\")]).t()\n",
    "            mlp_weight_1 = np2th(weights[pjoin(ROOT, FC_1, \"kernel\")]).t()\n",
    "            mlp_bias_0 = np2th(weights[pjoin(ROOT, FC_0, \"bias\")]).t()\n",
    "            mlp_bias_1 = np2th(weights[pjoin(ROOT, FC_1, \"bias\")]).t()\n",
    "\n",
    "            self.ffn.fc1.weight.copy_(mlp_weight_0)\n",
    "            self.ffn.fc2.weight.copy_(mlp_weight_1)\n",
    "            self.ffn.fc1.bias.copy_(mlp_bias_0)\n",
    "            self.ffn.fc2.bias.copy_(mlp_bias_1)\n",
    "\n",
    "            self.attention_norm.weight.copy_(np2th(weights[pjoin(ROOT, ATTENTION_NORM, \"scale\")]))\n",
    "            self.attention_norm.bias.copy_(np2th(weights[pjoin(ROOT, ATTENTION_NORM, \"bias\")]))\n",
    "            self.ffn_norm.weight.copy_(np2th(weights[pjoin(ROOT, MLP_NORM, \"scale\")]))\n",
    "            self.ffn_norm.bias.copy_(np2th(weights[pjoin(ROOT, MLP_NORM, \"bias\")]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### e. Attention"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "query key value\n",
    "\n",
    "1. 首先是让输入的embedding通过三个线性变换得到Qi, Ki, Vi\n",
    "2. 利用query和key进行点积，再除以根号d，再softmax，得到attention score\n",
    "3. 权重乘以value求和，再经过一个线性变换，得到输出"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "class Attention(nn.Module):\n",
    "    def __init__(self, config, vis):\n",
    "        super(Attention, self).__init__()\n",
    "        self.vis = vis\n",
    "        self.num_attention_heads = config.transformer[\"num_heads\"] # 注意力头数 12\n",
    "        self.attention_head_size = int(config.hidden_size / self.num_attention_heads)  # 注意力头的大小 768 / 12 = 64\n",
    "        self.all_head_size = self.num_attention_heads * self.attention_head_size # 768\n",
    "        # Wq, Wk, Wv\n",
    "        self.query = Linear(config.hidden_size, self.all_head_size)\n",
    "        self.key = Linear(config.hidden_size, self.all_head_size)\n",
    "        self.value = Linear(config.hidden_size, self.all_head_size)\n",
    "\n",
    "        self.out = Linear(config.hidden_size, config.hidden_size)\n",
    "        self.attn_dropout = Dropout(config.transformer[\"attention_dropout_rate\"])\n",
    "        self.proj_dropout = Dropout(config.transformer[\"attention_dropout_rate\"])\n",
    "\n",
    "        self.softmax = Softmax(dim=-1)\n",
    "\n",
    "    def transpose_for_scores(self, x):\n",
    "        # x: torch.Size([16, 197, 768])\n",
    "        new_x_shape = x.size()[:-1] + (self.num_attention_heads, self.attention_head_size)\n",
    "        # new_x_shape = (16, 197) + (12, 64) = (16, 197, 12, 64)\n",
    "        x = x.view(*new_x_shape)\n",
    "        return x.permute(0, 2, 1, 3)\n",
    "\n",
    "    def forward(self, hidden_states):\n",
    "        # hidden_states: torch.Size([16, 197, 768]) -> layer: torch.Size([16, 197, 768])\n",
    "        mixed_query_layer = self.query(hidden_states)\n",
    "        mixed_key_layer = self.key(hidden_states)\n",
    "        mixed_value_layer = self.value(hidden_states)\n",
    "                \n",
    "        # transpose for attention scores -> layer: torch.Size([16, 12, 197, 64])\n",
    "        query_layer = self.transpose_for_scores(mixed_query_layer)\n",
    "        key_layer = self.transpose_for_scores(mixed_key_layer)\n",
    "        value_layer = self.transpose_for_scores(mixed_value_layer)\n",
    "\n",
    "        # calculate attention scores -> attention_scores: torch.Size([16, 12, 197, 197])\n",
    "        attention_scores = torch.matmul(query_layer, key_layer.transpose(-1, -2))\n",
    "\n",
    "        # normalize the attention scores to probabilities -> attention_probs: torch.Size([16, 12, 197, 197])\n",
    "        attention_scores = attention_scores / math.sqrt(self.attention_head_size)\n",
    "        attention_probs = self.softmax(attention_scores)\n",
    "        weights = attention_probs if self.vis else None\n",
    "        attention_probs = self.attn_dropout(attention_probs)\n",
    "\n",
    "        # calculate the weighted value -> context_layer: torch.Size([16, 12, 197, 64])\n",
    "        context_layer = torch.matmul(attention_probs, value_layer)\n",
    "        context_layer = context_layer.permute(0, 2, 1, 3).contiguous()   # context_layer: torch.Size([16, 197, 12, 64])\n",
    "\n",
    "        # merge the attention heads -> context_layer: torch.Size([16, 197, 768])\n",
    "        new_context_layer_shape = context_layer.size()[:-2] + (self.all_head_size,)\n",
    "        context_layer = context_layer.view(*new_context_layer_shape)   # context_layer: torch.Size([16, 197, 768])\n",
    "\n",
    "        # linear layer -> layer: torch.Size([16, 197, 768])\n",
    "        attention_output = self.out(context_layer)\n",
    "        attention_output = self.proj_dropout(attention_output)\n",
    "        return attention_output, weights"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### f. MLP"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<center>\n",
    "<img src=\"./res/vit_mlp.png\" width=\"15%\">\n",
    "</center>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "ACT2FN = {\"gelu\": torch.nn.functional.gelu, \"relu\": torch.nn.functional.relu, \"swish\": swish}\n",
    "class Mlp(nn.Module):\n",
    "    def __init__(self, config):\n",
    "        super(Mlp, self).__init__()\n",
    "        self.fc1 = Linear(config.hidden_size, config.transformer[\"mlp_dim\"])\n",
    "        self.fc2 = Linear(config.transformer[\"mlp_dim\"], config.hidden_size)\n",
    "        self.act_fn = ACT2FN[\"gelu\"]\n",
    "        self.dropout = Dropout(config.transformer[\"dropout_rate\"])\n",
    "\n",
    "        self._init_weights()\n",
    "\n",
    "    def _init_weights(self):\n",
    "        # 初始化权重\n",
    "        nn.init.xavier_uniform_(self.fc1.weight)\n",
    "        nn.init.xavier_uniform_(self.fc2.weight)\n",
    "        nn.init.normal_(self.fc1.bias, std=1e-6)\n",
    "        nn.init.normal_(self.fc2.bias, std=1e-6)\n",
    "\n",
    "    def forward(self, x):\n",
    "        x = self.fc1(x)\n",
    "        x = self.act_fn(x)\n",
    "        x = self.dropout(x)\n",
    "        x = self.fc2(x)\n",
    "        x = self.dropout(x)\n",
    "        return x"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### g. Transformer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "class Transformer(nn.Module):\n",
    "    def __init__(self, config, img_size, vis):\n",
    "        super(Transformer, self).__init__()\n",
    "        self.embeddings = Embeddings(config, img_size=img_size)\n",
    "        self.encoder = Encoder(config, vis)\n",
    "\n",
    "    def forward(self, input_ids):\n",
    "        # 每个patch都会对应一个id，嵌入到向量空间\n",
    "        embedding_output = self.embeddings(input_ids)\n",
    "        encoded, attn_weights = self.encoder(embedding_output)\n",
    "        return encoded, attn_weights"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Vision Transformer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "class VisionTransformer(nn.Module):\n",
    "    def __init__(self, config, img_size=224, num_classes=21843, zero_head=False, vis=False):\n",
    "        '''\n",
    "        img_size: 输入图片的尺寸\n",
    "        num_classes: 分类的类别数\n",
    "        zero_head: 是否初始化头部为0\n",
    "        vis: 是否可视化注意力权重\n",
    "        '''\n",
    "        super(VisionTransformer, self).__init__()\n",
    "        self.num_classes = num_classes\n",
    "        self.zero_head = zero_head\n",
    "        self.classifier = config.classifier\n",
    "\n",
    "        self.transformer = Transformer(config, img_size, vis)\n",
    "        self.head = Linear(config.hidden_size, num_classes)\n",
    "\n",
    "    def forward(self, x, labels=None):\n",
    "        x, attn_weights = self.transformer(x)\n",
    "        logits = self.head(x[:, 0])  # 取cls token的输出\n",
    "\n",
    "        if labels is not None:\n",
    "            loss_fct = CrossEntropyLoss()\n",
    "            loss = loss_fct(logits.view(-1, self.num_classes), labels.view(-1))\n",
    "            return loss\n",
    "        else:\n",
    "            return logits, attn_weights\n",
    "        \n",
    "    def load_from(self, weights):\n",
    "        with torch.no_grad():\n",
    "            if self.zero_head:\n",
    "                nn.init.zeros_(self.head.weight)\n",
    "                nn.init.zeros_(self.head.bias)\n",
    "            else:\n",
    "                self.head.weight.copy_(np2th(weights[\"head/kernel\"]).t())\n",
    "                self.head.bias.copy_(np2th(weights[\"head/bias\"]).t())\n",
    "\n",
    "            self.transformer.embeddings.patch_embeddings.weight.copy_(np2th(weights[\"embedding/kernel\"], conv=True))\n",
    "            self.transformer.embeddings.patch_embeddings.bias.copy_(np2th(weights[\"embedding/bias\"]))\n",
    "            self.transformer.embeddings.cls_token.copy_(np2th(weights[\"cls\"]))\n",
    "            self.transformer.encoder.encoder_norm.weight.copy_(np2th(weights[\"Transformer/encoder_norm/scale\"]))\n",
    "            self.transformer.encoder.encoder_norm.bias.copy_(np2th(weights[\"Transformer/encoder_norm/bias\"]))\n",
    "\n",
    "            posemb = np2th(weights[\"Transformer/posembed_input/pos_embedding\"])\n",
    "            posemb_new = self.transformer.embeddings.position_embeddings\n",
    "            if posemb.size() == posemb_new.size():\n",
    "                self.transformer.embeddings.position_embeddings.copy_(posemb)\n",
    "            else:\n",
    "                print(\"load_pretrained: resized variant: %s to %s\" % (posemb.size(), posemb_new.size()))\n",
    "                ntok_new = posemb_new.size(1)\n",
    "\n",
    "                if self.classifier == \"token\":\n",
    "                    posemb_tok, posemb_grid = posemb[:, :1], posemb[0, 1:]\n",
    "                    ntok_new -= 1\n",
    "                else:\n",
    "                    posemb_tok, posemb_grid = posemb[:, :0], posemb[0]\n",
    "\n",
    "                gs_old = int(np.sqrt(len(posemb_grid)))\n",
    "                gs_new = int(np.sqrt(ntok_new))\n",
    "                print('load_pretrained: grid-size from %s to %s' % (gs_old, gs_new))\n",
    "                posemb_grid = posemb_grid.reshape(gs_old, gs_old, -1)\n",
    "\n",
    "                zoom = (gs_new / gs_old, gs_new / gs_old, 1)\n",
    "                posemb_grid = ndimage.zoom(posemb_grid, zoom, order=1)\n",
    "                posemb_grid = posemb_grid.reshape(1, gs_new * gs_new, -1)\n",
    "                posemb = np.concatenate([posemb_tok, posemb_grid], axis=1)\n",
    "                self.transformer.embeddings.position_embeddings.copy_(np2th(posemb))\n",
    "\n",
    "            for bname, block in self.transformer.encoder.named_children():\n",
    "                for uname, unit in block.named_children():\n",
    "                    unit.load_from(weights, n_block=uname)\n",
    "\n",
    "            if self.transformer.embeddings.hybrid:\n",
    "                self.transformer.embeddings.hybrid_model.root.conv.weight.copy_(np2th(weights[\"conv_root/kernel\"], conv=True))\n",
    "                gn_weight = np2th(weights[\"gn_root/scale\"]).view(-1)\n",
    "                gn_bias = np2th(weights[\"gn_root/bias\"]).view(-1)\n",
    "                self.transformer.embeddings.hybrid_model.root.gn.weight.copy_(gn_weight)\n",
    "                self.transformer.embeddings.hybrid_model.root.gn.bias.copy_(gn_bias)\n",
    "\n",
    "                for bname, block in self.transformer.embeddings.hybrid_model.body.named_children():\n",
    "                    for uname, unit in block.named_children():\n",
    "                        unit.load_from(weights, n_block=bname, n_unit=uname)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 3. load pretrained model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
    "model = VisionTransformer(config, IMG_SIZE, zero_head=True, num_classes=100)\n",
    "model.load_from(np.load(\"/content/ViT-B_16.npz\"))\n",
    "model.to(device)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 4. train the model\n",
    "(in colab)"
   ]
  }
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